Monte Carlo Simulation Analysis Research Articles

Axial compressive behavior of corroded H-shaped steel columns

The unpredictability of corrosion in steel structures, especially in H-section columns, is a topic that has received limited research attention. Using Monte Carlo simulation (MCS) and finite element analysis (FEA), this study investigates the effect of random pitting corrosion on H-section steel columns' ultimate bearing capacity under axial compression. In this study, MCS was employed to address the random distribution of pitting corrosion. The reliability of MCS and FEA was validated through existing literature on compression tests of H-shaped steel columns. To comprehensively assess the detrimental effects of pitting corrosion, a total of 3650 models of H-shaped columns, each exhibiting different pitting corrosion characteristics, were subjected to analysis. These models incorporated three corrosion parameters (volume loss rate, size, and depth) and two geometric parameters (slenderness and section size). A nonlinear buckling analysis was carried out to investigate the ultimate bearing capacity of the columns. The findings indicate that a volume loss rate of 20% can cause a decrease in the column's ultimate bearing capacity by approximately 50%. Furthermore, the study reveals that at equal volume loss rates, corrosion depth and slenderness significantly influence the reduction factor, while corrosion size and section size exert a minimal impact. As a result, this paper introduces a probabilistic assessment method for estimating the reduction factor in ultimate bearing capacity for H-section steel columns. Notably, with every 10% increase in the volume loss rate, the mean reduction factor in the ultimate bearing capacity decreases by approximately 0.12.

Narrowly Banded Spectra with Peak Frequency around 1 GHz of FRB 20201124A: Implications for Energy Function and Radiation Physics

The radiation physics of fast radio bursts (FRBs) remains an open question. Current observations have discovered that narrowly banded bursts of FRB 20201124A are active in 0.4–2 GHz, and their spectral peak frequency ( νpobs ) is mostly toward ∼1 GHz. Utilizing a sample of 1268 bursts of FRB 20201124A detected with the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we show that the 1σ spectral regime of 71.4% of the events (in-band bursts) is within the FAST bandpass. The intrinsic burst energies ( EBWeobs ) and spectral widths ( σsobs ) are well measured by fitting the spectral profile with a Gaussian function. The derived EBWeobs and σsobs distributions are lognormal and centered at logEBWeobs/erg=37.2(σ=0.76) and logσsobs/GHz=−1.16(σ=0.17) . Our Monte Carlo simulation analysis infers its intrinsic ν p distribution as a normal function centered at ν p,c = 1.16 GHz (σ = 0.22) and its intrinsic energy function as Φ(E)∝E−0.60e−E/Ec with E c = 9.49 × 1037 erg. We compare these results with that of typical repeating FRBs 20121102A and 20190520B, which are active over a broad frequency range at several specific frequencies, and discuss possible observational biases on the estimation of the event rate and energy function. Based on these results, we argue that FRB 20201124A likely occurs in a fine-tuned plasma for maser radiation at a narrow frequency range, while FRB 20121102A and FRB 20190520B could involve clumpy plasma conditions that make maser emission around several specific frequencies in a broad range.

Scheduling Analysis with Monte Carlo Simulation and Statistic Method on a Building Construction Project

In the construction of an office building in Indonesia, a delay occurred in the first-floor concrete work. The project was planned to be completed in 161 days but it took 168 days to complete. This study analyzed the issues that arose by doing a scheduling analysis and by comparing the Monte Carlo Simulation and the Statistic method. A comparison between these methods was carried out to determine the project duration scheduling probability level from each method. The data included in the study were the project’s Time Schedule and the interview data from respondents who were directly related to the office building construction project. Monte Carlo Simulation analysis showed that to get a 100% probability level, the duration can only be accelerated by two days, from 168 days to 166 days, while the Statistical method analysis showed that to get a 99.99% probability level the duration can only be accelerated by 4,2 days, from 168 days to 163.8 days. From the results above, it can be concluded that the Monte Carlo Simulation has a more accurate calculation than the Statistic Method because the result from the Monte Carlo Simulation was 166 days with a 100% probability level. It is close to the project realization duration which was 168 days.
 Keywords: construction delay analysis, monte carlo simulation, statistic method comparison

An extensive conformable fractional grey model and its application

In order to expand the applicability of the conventional conformable fractional grey model, an extensive conformable fractional grey model, abbreviated as ECFGM(1,1), is proposed by the introduction of the extensive conformable fractional accumulation. Specifically, the improvements of the proposed model can be outlined as follows. First, the extensive fractional accumulation and difference are designed for accumulated generating operation and its reverse calculation, respectively. Second, based on the extensive form, the parameter estimation and time response function of the ECFGM(1,1) model are deduced, thereinto, the particle swarm optimization algorithm is employed to determine the optimal fractional order for the newly-designed model. It is worthy noting that an algorithm framework by the Monte Carlo simulation and parameter sensitivity analysis is conducted to assess the robustness of the proposed model. To validate this model’s efficacy, the novel technique is adopted to forecast China’s primary energy consumption compared with a series of competitive models. The numerical results indicate the newly-proposed model is superior to all competitors in terms of MAPE and RMSE values, thus, the proposed ECFGM(1,1) model is considered a powerful and promising method for enhancing the existing fractional grey models.

Health risk assessment of soil heavy metals in a typical mining town in north China based on Monte Carlo simulation coupled with Positive matrix factorization model

The accumulation of heavy metals (HMs) in soil caused by mineral resource exploitation and its ancillary industrial processes poses a threat to ecology and public health. Effective risk control measures require a quantification of the impacts and contributions to health risks from individual sources of soil HMs. Based on high-density sampling, soil contamination risk indexes, positive matrix factorization (PMF) model, Monte Carlo simulation and human health risk analysis model were applied to investigate the risk of HMs in a typical mining town in North China. The results showed that As was the most dominant soil pollutant factor, Cd and Hg were the most dominant soil ecological risk factors, and Cr and Ni were the most dominant health risk factors in the study area. Overall, both pollution and ecological risks were at low levels, while there were still some higher hazard areas located in the central and south-central part of the region. According to the probabilistic health risk assessment (HRA), children suffered greater health risks than adults, with 21.63% of non-carcinogenic risks and 53.24% of carcinogenic risks exceeding the prescribed thresholds (HI > 1 and TCR>1E-4). The PMF model identified five potential sources: fuel combustion (FC), processing of building materials with limestone as raw materials (PBML), industry source (IS), iron ore mining combined with garbage (IOG), and agriculture source (AS). PBML is the primary source of soil HM contamination, as well as the major anthropogenic source of carcinogenic risk for all populations. Agricultural inputs associated with As are the major source of non-carcinogenic risk. This study offers a good example of probabilistic HRA using specific sources, which can provide a valuable reference for strategy establishment of pollution remediation and risk prevention and control.

Production risk analysis and prioritization in manufacturing industry: A hybrid approach using Monte Carlo simulation and DEMATEL analysis

Risk identification and prioritization in production sector is critical if an enterprise wishes to compete successfully. To achieve this, Delphi technique was utilized to finalize 36 identified risks in a ‘metal plate production’ stage prior to risk analysis through risk probability and its impact. Risk prioritization is performed by Monte Carlo Simulation (MCS) and Decision Making and Trial Evaluation Laboratory (DEMATEL) analysis. The novelty of this study is to develop an effective hybrid approach by utilizing strengths of MCS (with iterations in thousands on the simulation) as well as DEMATEL analysis (to assess the causal relationship among risk factors). A more accurate risk identification by developing a consolidated list of risks in one of the important production stages followed by an efficient prioritization of interacting (process and product related) risks will help the shop floor operator making better decisions to enhance the quality by minimizing production time and cost.

A novel extension of half-logistic distribution with statistical inference, estimation and applications

In the present study, we develop and investigate the odd Frechet Half-Logistic (OFHL) distribution that was developed by incorporating the half-logistic and odd Frechet-G family. The OFHL model has very adaptable probability functions: decreasing, increasing, bathtub and inverted U shapes are shown for the hazard rate functions, illustrating the model’s capacity for flexibility. A comprehensive account of the mathematical and statistical properties of the proposed model is presented. In estimation viewpoint, six distinct estimation methodologies are used to estimate the unknown parameters of the OFHL model. Furthermore, an extensive Monte Carlo simulation analysis is used to evaluate the effectiveness of these estimators. Finally, two applications to real data are used to demonstrate the versatility of the suggested method, and the comparison is made with the half-logistic and some of its well-known extensions. The actual implementation shows that the suggested model performs better than competing models.

A full‐probabilistic cloud analysis for structural seismic fragility via decoupled M‐PDEM

AbstractPerformance‐based earthquake engineering (PBEE) is essential for ensuring engineering safety. Conducting seismic fragility analysis within this framework is imperative. Existing methods for seismic fragility analysis often rely heavily on double loop reanalysis and empirical data fitting, leading to challenges in obtaining high‐precision results with a limited number of representative structural analysis instances. In this context, a new methodology for seismic fragility based on a full‐probabilistic cloud analysis is proposed via the decoupled multi‐probability density evolution method (M‐PDEM). In the proposed method, the assumption of a log‐normal distribution is not required. According to the random event description of the principle of preservation of probability, the transient probability density functions (PDFs) of intensity measure (IM) and engineering demand parameter (EDP), as key response quantities of the seismic‐structural system, are governed by one‐dimensional Li‐Chen equations, where the physics‐driven forces are determined by representative analysis data of the stochastic dynamic system. By generating ground motions based on representative points of basic random variables and performing structural dynamic analysis, the decoupled M‐PDEM is employed to solve the one‐dimensional Li‐Chen equations. This yields the joint PDF of IM and EDP, as well as the conditional PDF of EDP given IM, resulting in seismic fragility analysis outcomes. The numerical implementation procedure is elaborated in detail, and validation is performed using a six‐story nonlinear reinforced concrete (RC) frame subjected to non‐stationary stochastic ground motions. Comparative analysis against Monte Carlo simulation (MCS) and traditional cloud analysis based on least squares regression (LSR) reveals that the proposed method achieves higher computational precision at comparable structural analysis costs. By directly solving the physics‐driven Li‐Chen equations, the method provides the full‐probabilistic joint information of IM and EDP required for cloud analysis, surpassing the accuracy achieved by traditional methods based on statistical moment fitting and empirical distribution assumptions.

A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China’s total energy consumption, China’s total electricity generation and China’s total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China.

Control variates with splitting for aggregating results of Monte Carlo simulation and perturbation analysis

Estimation of second-order statistics allows characterizing the uncertainty associated with the response of stochastic finite element models. Two common approaches for estimating these statistics are Monte Carlo simulation and perturbation. The purpose of this paper is to present a framework to aggregate the results obtained by means of these two approaches under the umbrella of Control Variates with Splitting. This allows to produce estimates of the second-order statistics of the system’s response with improved precision and accuracy. More specifically, Control Variates is implemented in such a way that the variance of the estimates of second-order statistics is minimized. In addition, the application of intervening variables for enhancing perturbation is considered as well, showing substantial advantages by increasing the accuracy of the estimates of second-order statistics. The application of the proposed framework is illustrated by means of an example involving the estimation of second-order statistics of a model involving confined seepage flow.

[Health Risk Assessment and Priority Control Factors Analysis of Heavy Metals in Agricultural Soils Based on Source-oriented].

To analyze the source apportionment and health risk of heavy metals in agricultural soils of major producing areas of agricultural products in Chongqing, a positive matrix factorization (PMF) model and health risk assessment (HRA) model based on Monte Carlo simulation were used. Meanwhile, both the PMF and HRA model were combined to explore health risks of heavy metals in agricultural soils by different pollution sources in order to determine the priority control factors. The results showed that the average values of Cd concentration were higher than its corresponding background value; the average values of Cr concentration were lower than its corresponding background value; and the average values of As, Pb, Cu, Ni, and Zn concentration were basically consistent with their corresponding background values. Using PMF model analysis, natural sources, industrial sources, and agricultural sources were identified as the determinants for the accumulation of heavy metals in agricultural soils, with the contribution rates of 35%, 24%, and 41%, respectively. Using the HRA model based on Monte Carlo simulation analysis, carcinogenic risks of adult and children were tolerable (1.00E-6 < TCR ≤ 1.00E-4), whereas non-carcinogenic risks were acceptable (HI ≤ 1). Oral ingestion was the main exposure pathway. The analysis results of the relationship among heavy metals, pollution sources, and health risks showed that industrial pollution and As were identified as priority control factors, and agricultural pollution and Cd were identified as secondary control factors. Our findings provide scientific support for decision makers to control soil pollution and reduce the management costs of soil pollution.

Decomposition of agriculture-related non-CO2 greenhouse gas emissions in Chengdu: 1995–2020

Methane (CH4) and nitrous oxide (N2O) emissions originating from agricultural systems exert a significant influence on climate change. The exploration of potential driving factors and emission trends based on a city is important for policy-makers and land managers, however has been rarely known. We aim to investigate the potential driving factors behind five non-CO2 sources within Chengdu's agricultural sector spanning 1995 to 2020, including rice cultivation (CH4), agricultural land (N2O), manure management (CH4 and N2O), and enteric fermentation (CH4), and project non-carbon dioxide (CO2) greenhouse gas (GHG) emissions in Chengdu's agricultural sector for the period from 2021 to 2035. The logarithmic mean Divisia index (LMDI) decomposition model was adopted to figure out the influencing factors, while Monte Carlo simulation and scenario analysis were applied to predict the agricultural non-CO2 GHG emissions from 2021 to 2035. Results reveal that non-CO2 GHG emissions from Chengdu's agriculture exhibit fluctuations and declines, with N2O emissions from agricultural land being the most substantial source. Driving actors such as emission intensity (EI), agricultural industrial structure (IS), and rural population (RP) contribute to carbon emission reductions, resulting in reductions of 8.10, 4.25, and 0.91 million tons (Mt), respectively. Conversely, the regional economic development level (EDL), and urbanization rate (URB) are the primary drivers behind increased agricultural non-CO2 GHG emissions, leading to emissions of 8.91 and 2.35 Mt, respectively. Ultimately, the predictive analysis demonstrates that, under the technological breakthrough scenario, Chengdu's agricultural non-CO2 GHG emissions are projected to peak in 2025, with an expected value of only 2.28 Mt. This study offered practical guidance for mitigating non-CO2 GHG emissions in city-level agriculture.

Combination of the modified Kibria–Lukman and the principal component regression estimators

Statistical inference with the ordinary least squares (OLS) estimator is frequently influenced when there is a multicollinearity in the linear regression model. In this article, to reduce these effects of multicollinearity, we generalize the modified Kibria–Lukman principal component (MKLPC) estimator in the linear regression model by combining the principal component regression (PCR) estimator and the modified Kibria–Lukman (MKL) estimator. Meanwhile, the necessary and sufficient conditions for the superiority of the MKLPC estimator over OLS, PCR, Ridge, r-k, Liu, r-d, k-d, KL, and MKL estimators in the mean squared error (MSE) criterion are derived. Furthermore, we conduct Monte Carlo simulation and empirical analysis to compare these estimators under the MSE criterion.

Seasonal variations of potentially toxic elements (PTEs) in drinking water and health risk assessment via Monte Carlo simulation and Sobol sensitivity analysis in southern Iran's largest city

The escalating concern over the presence and health implications of potentially toxic elements (PTEs) in drinking water has underscored the need for rigorous risk assessments. Our study aimed to quantify both the non-carcinogenic and carcinogenic health risks associated with exposure to selected PTEs—namely arsenic (As), chromium (Cr), and cadmium (Cd). Also, we evaluated ingestion and skin contact exposures to risks during summer and winter using metrics such as the hazard quotient (HQ), hazard index (HI), and cancer risk (CR) for children, adult males, and adult females. For all demographic groups and exposure pathways, the HQ values remain below the established safety threshold (HQ < 1). Notably, As consistently had the highest average HI value across children, male adults, and female adults. Seasonal variations were statistically significant (p < 0.05) for As and Cr, but not Cd. During the summer, the average total carcinogenic risks (TCR) from drinking water exposure were 7.61 × 10–6, 8.94 × 10–6, and 1.12 × 10–5 for children, male adults, and female adults, respectively. In the winter, these values were 1.18 × 10–5, 1.40 × 10–5, and 1.75 × 10–5, respectively. The fuzzy C-means clustering analysis provided insights into our dataset's Cr, Cd, and As distribution patterns. Results indicate that As, Cr, and Cd mean concentrations were below the World Health Organization health-based guidelines. The CR values for children and adults from drinking water exposure were slightly above or below the US Environmental Protection Agency’s standards. These findings can inform research and policy-making regarding the risk of PTEs in drinking water and highlight the need to monitor Shiraz water regularly.

Response of Agriculture Production to Change of Foreign Direct Investment and Public Agriculture Expenditure in South Africa: A Monte Carlo Simulation Analysis

The rationale of this paper was to investigate the response of agriculture production to the simultaneous shock of foreign direct investment and public agricultural spending in South Africa during 1991-2019. Data were collected from secondary sources and analyzed using Monte Carlo simulation. Results revealed that agriculture production is maximized at 1,73% if Foreign direct investment inflows, agricultural credit, and the number of employees increase by 10% while public spending is decreased by 10%. Hence, it is recommended that policymakers should combine FDI inflows in agriculture, and agricultural credit in a complementary manner, with emphasis to attract more extensive farm workers to ensure the sustainability of production in the agriculture sector in South Africa. This paper contributes to enhance agriculture sector by using the best combination of input in agriculture in order to maximize production.

Estimation of risk to soil and human health during irrigation using ZnO nanoparticles-containing water

This work estimated ecological and human health risks for the exposure pathway of spinach irrigation with metal oxide nanoparticles- contaminated river water. Using literature studies, log-logistic plant uptake models were developed to predict metal uptake in plant parts. Reported or expected values of ZnO nanoparticles (NPs) in surface water were collected from published literature for realistic scenarios and used to estimate the risk to human health in terms of hazard quotient (HQ). Monte Carlo simulation and uncertainty analysis were conducted to assess the variability in the obtained results. The Hazard Quotient (HQ) values obtained at environmental concentrations of ZnO nanoparticles were all below 1 for all age groups. Although the 99th percentile value of HQ was found to be the highest (3.57 ×10−3) for the age group (1−6) years, the probability of HQ value being greater than unity was found to be null for all age groups. The 99th percentile values of contamination factor, potential ecological risk factor, and geo-accumulation index, computed as part of ecological risk assessment, were found to be 0.098, 0.098, and − 0.62, respectively, well within their respective contamination limits. Bio-concentration and translocation factors were also computed to quantify the internal uptake of the plant. The results implied that at the current levels of zinc oxide nanoparticles in surface water, no health risk would be exerted on humans upon spinach consumption under the assumptions used. Also, negligible chances of soil contamination were observed due to zinc accumulation after irrigation. The approach proposed in this work can be used to determine potential health and ecological risks when reusing metal oxide NP-contaminated water for irrigating edible crops. This especially gains significance in a future perspective due to a likely increase in nanoparticles' concentration in the environmental media.

Arsenic and fluoride exposure in drinking water caused human health risk in coastal groundwater aquifers

Groundwater (GW) is a precious resource for human beings as we depend on it as a source of fresh drinking water, agricultural practices, industrial and domestic uses, etc. Extreme exposure of arsenic (As) and fluoride (F-) concentrations along the coastal GW aquifers of “South 24 Parganas and East Medinipur” diluted the quality of GW and created serious health issues. Various chronic health disorders such as – black foot disease, fluorosis skin cancer, cardiac problems, and other water borne diseases have been noticed in these two coastal districts. The comprehensive entropy-weighted water quality index (EWQI) and health risk assessment (HRA) were applied to evaluate the quality of GW and probable health risks in the coastal districts. Monte Carlo simulation and sensitivity analysis methods were simultaneously adopted to identify the non-carcinogenic health risk assessment due to regular ingestion of contaminated GW. As the study region is densely populated and part of the Sundarbans Ramsar site, it has greater importance at the international level along with regional importance to address the GWQ of this region. The major findings of the present study highlight that almost 55% of the study area is confronting serious GW quality issues and associated probable health risk (HR) due to the intense accumulation of As and F- in the GW aquifers of the study area. Children's health is more vulnerable due to the consumption of As containing GW, and adults are highly affected due to the intake of F- bearing GW in the coastal districts. The findings of the current study will draw the attention of hydrologists, groundwater management authorities, government bodies, and NGOs to regulate and monitor the GW aquifers routinely, enhance GW quality, minimizing the health hazards and sustainable water management in a more scientific and sustainable way which must be advantageous for coastal people.

Cost-Effectiveness Analysis of Adjuvant Endocrine Therapy With Ovarian Suppression in Premenopausal Patients With Hormone Receptor-Positive Early Breast Cancer in China

PurposeEndocrine therapy combined with ovarian function suppression (OFS) is recommended in intermediate- or high-risk patients among premenopausal women with hormone receptor-positive early breast cancer. However, in China, the cost-effectiveness of this strategy compared with endocrine therapy alone is unclear. This study aimed to evaluate the long-term cost-effectiveness of tamoxifen (TAM), TAM+OFS, and exemestane plus OFS (EXE+OFS). MethodsOn the basis of prognostic data from the Suppression of Ovarian Function Trial (SOFT), cost data from the Hospital Information System of the West China Hospital of Sichuan University, and health utility values from the published literature, a Markov model was established. The incremental cost-effectiveness ratio (ICER) was used to compare the treatment strategies. ResultsIn a 25-year simulation of adjuvant therapy in Chinese women with early breast cancer, the total costs of TAM, TAM+OFS, and EXE+OFS were $7821, $9318, and $9445, respectively. The quality-adjusted life-years (QALYs) were 11.615, 11.896, and 11.734 years, respectively. Compared with TAM, the ICERs of TAM+OFS and EXE+OFS were $5,327.4021/QALY and $13,647.0588/QALY, respectively. The ICERs of TAM+OFS and EXE+OFS were below the threshold of $32,517/QALY. The reliability and stability of the simulation results were verified using Monte Carlo simulation and sensitivity analysis. ConclusionIn the context of limited resources in China, TAM+OFS and EXE+OFS are cost-effective options compared with TAM.

Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

PurposeA progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.Design/methodology/approachIn this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.FindingsThe Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.Originality/valueThe ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.

Accurate forecasts and comparative analysis of Chinese CO2 emissions using a superior time-delay grey model

In China's new development stage, reaching carbon peaking and neutrality has emerged as a complicated and substantial task, highlighting the importance of forecasting CO2 emissions. Therefore, this research designs a novel convolution-based multivariate time-delay grey model that integrates time-delay coefficients and analytical solutions, thus accurately identifying and quantifying time-delay effects. Moreover, we present an algorithm determination framework to enhance the model's applicability and flexibility, and assess the robustness through the Monte Carlo Simulation and parameter sensitivity analysis. For demonstration purposes, the novel technique is utilized to forecast Chinese CO2 emissions, yielding the lowest MAPE values (1.80%for three-steps ahead, 1.51% for two-steps ahead, and 0.27% for one-step ahead) compared with traditional grey and non-grey competitor models. Additionally, the Monte Carlo Simulation results reveal that the particle swarm optimization algorithm outperforms other candidate algorithms due to the highest accuracy and optimal stability. Overall, the newly-proposed approach, possessing more powerful flexibility and applicability than the previous models, is an effective technique for forecasting CO2 emissions in China.